The pathogenesis of metabolic dysfunction-associated steatotic liver disease （MASLD） is fundamentally rooted in spleen deficiency and is closely associated with phlegm turbidity， damp-heat and blood stasis. Clinically， liver constraint with spleen deficiency and internal retention of damp turbidity represent the predominant traditional Chinese medicine （TCM） syndrome patterns. Researches have indicated intrinsic connections between the syndrome patterns and biological indicators such as gut microbiota and metabolic profiles. Regarding treatment， classical famous formulas， modern empirical formulas， and newly developed TCM drugs show positive effects in regulating glucose and lipid metabolism， improving insulin resistance， and alleviating metabolic inflammation， exhibiting multi-target mechanisms of action； acupuncture and other external therapies also provide adjunctive value. Nevertheless， current researches still have limitations such as the lack of high-quality clinical evidence and insufficient systematic elucidation of the uncerlying mechanisms. Future efforts should focus on conducting high-quality TCM clinical trials with hard endpoint outcomes such as hepatic histology outcomes， and utilizing modern technologies like multi-omics to elucidate TCM's mechanisms of action， thereby advancing the position of TCM as a first-line therapeutic strategy for MASLD.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

ObjectiveIn the context of the digital transformation of education, this study aims to construct a triadic interactive teaching model for surgical animal surgery in clinical medicine using modern information technology. It explores the effectiveness of different teaching methods in improving students' practical skills, aseptic awareness, and teamwork abilities, providing a reference for the reform of clinical practice education. MethodsA quasi-experimental research design was adopted. A total of 80 students from the eight-year clinical medicine program at Nanjing University were selected, including the Class of 2020 (control group, n=40) and the Class of 2021 (experimental group, n=40). The control group received traditional teaching methods, while the experimental group implemented the "Teacher-Student-AI" triadic interactive teaching model. This model utilized a smart teaching platform for personalized pre-class preparation , as well as data-driven post-class review and feedback throughout the entire teaching process. The "assessment indicators and scoring criteria for the surgical animal surgery course" were used to evaluate teaching effectiveness, with independent samples t-tests used for statistical analysis. ResultsPre-course assessments revealed no statistically significant differences in baseline theoretical knowledge or practical skills between the two groups (P>0.05). Upon completion of the course, the experimental group achieved higher scores than the control group across three key dimensions: practical skills (47.98±1.34 vs 46.92±2.51, P=0.022), aseptic awareness (17.84±1.16 vs 16.94±2.29, P=0.029), and teamwork (16.82±1.44 vs 15.95±1.22, P=0.004). However, no statistically significant difference was observed in the scores for humane care awareness between the two groups (8.24±0.70 vs 8.16±0.53, P=0.589). ConclusionThe AI-based triadic interactive teaching model can, to some extent, address the limitations of traditional surgical animal surgery education. It plays a positive role in enhancing medical students' surgical skills, aseptic awareness, and collaborative abilities. This model facilitates the transition from traditional to personalized teaching and offers a practical framework for the digital reform of clinical practice education.

Methods:This study was a cross-sectional study. A prospective cohort study enrolled 42 patients with clinically suspected acute cerebrovascular disease and those undergoing follow-up examinations after intracranial vascular stenting at the First Affiliated Hospital of Zhengzhou University from June 2024 to May 2025. All patients underwent UHR PCD-CT examinations of the head and neck. Reconstructions were performed based on raw data, yielding conventional standard resolution (SR group) reconstructions and UHR images reconstructed using four distinct convolution kernels (Hv40, Hv48, Hv56, Hv64) in separate groups (Hv40 UHR group, Hv48 UHR group, Hv56 UHR group, Hv64 UHR group). Regions of interest were selected in the anterior cerebral artery, middle cerebral artery, posterior cerebral artery, posterior communicating artery, and anterior communicating artery. CT values and standard deviation (SD) values were measured for each artery, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Additionally, the sharpness of the vessel edges and the full-width-half-maximum (FWHM) of each artery were measured. One-way analysis of variance or the nonparametric Kruskal-Wallis test was used to compare the subjective and objective image quality metrics across the five groups. Pairwise comparisons were performed using the LSD test or Dunn method.Results:Statistically significant differences were observed in the overall comparison of vascular imaging SD, SNR, CNR, vascular edge sharpness, and FWHM among the SR group, Hv40 UHR group, Hv48 UHR group, Hv56 UHR group, and Hv64 UHR group ( P<0.05). No statistically significant differences in CT values were found ( P>0.05). Pairwise comparisons revealed statistically significant differences between all groups ( P<0.05), except that no significant differences were observed in image SD, SNR, CNR, vascular edge sharpness, or FWHM between the Hv56 UHR and Hv64 UHR groups ( P>0.05). Conclusions:UHR PCD-CT provides better image quality for neurovascular imaging. For the display of small intracranial vessels, the Hv64 provides sharper vessel walls and better subjective image quality compared to the less sharp convolutional cores.Objective:To explore the value of ultra-high resolution (UHR) photon-counting detector CT (PCD-CT) to improve the quality of neurovascular images.

Objective To explore the factors that influence major adverse cardiovascular events(MACE)in atrial fibrillation(AF)patients undergoing radiofrequency ablation(RFA),as well as to compare the prognosis of patients with advanced AF to that of the general population.Methods We prospectively recruited AF patients who underwent RFA treatment at Peking University Third Hospital between January 2021 and March 2023.General patient data were collected through the hospital's inpatient system,and MACE were tracked through outpatient visits and telephone follow-ups.Patients were categorized into three age groups:Group 1(under 65 years),Group 2(65 to 75 years),and Group 3(over 75 years).In this study,MACE was defined as include cardiovascular death,all-cause death,readmission for heart failure,acute coronary syndrome(ACS),grade 2 or higher bleeding and stroke.Results A total of 431 patients were included in this study,with an average age of(66.17±12.22)years.Among these patients,259 were male(60.09％),and the mean of CHA2DS2-VASc score was(1.79±1.30).The median follow-up period was 16.0(11.3,21.3)months,during which 28(6.50％)patients experienced MACE,with ACS and stroke being the most common events.Variables were selected using LASSO regression,and a LASSO-Cox regression model was constructed.Age(HR 1.06,95％CI 1.02-1.10,P=0.006)and hypertrophic cardiomyopathy(HR 3.70,95％CI 1.27-8.68,P=0.008)were identified as independent predictors of MACE after RFA for AF.Subgroup analysis revealed that patients under 65 had significantly better prognoses compared to older AF patients(P=0.030 compared with group 2;P=0.021 compared with group 3).Conclusions Age and hypertrophic cardiomyopathy are independent risk factors for MACE in AF patients undergoing RFA.The prognosis for younger patients is better than that for older patients,while the prognosis for advanced patients is comparable to that of patients aged 65-75 years.

OBJECTIVE To design and synthesize rifamycin S derivatives and load them into milk exosomes to evaluate their in vitro antimicrobial activity.METHODS Rifamycin S derivatives were synthe-sized and characterized by mass spectrometry and NMR.Using the dilution assay method,the inhibitory activity of each rifamycin S derivatives molecule against Staphylococcus aureus and Pseudomonas aerugi-nosa was determined,and the IC50 was calculated.Derivatives molecules with excellent antimicrobial activity were selected and loaded into milk exosomes using the ultrasonication method,resulting in the preparation of milk exosome-loaded rifamycin S derivatives.The antimicrobial activity against Staphylo-coccus aureus was determined using the dilution assay method.The inhibitory effect of the exosome-loaded rifamycin S derivatives on Staphylococcus aureus residing within macrophages was detected using the plate colony counting method.RESULTS Three rifamycin S derivatives were successfully designed and synthesized,which demonstrated superior antimicrobial activity against Staphylococcus aureus(the parent compound's antimicrobial activity is merely from 1/20 to 1/80 of that of the three rifamycin S derivatives)and Pseudomonas aeruginosa(the parent compound's antimicrobial activity is only 1/14 and 1/9 of that of compound 1 and compound 3)compared to the parent compound.The loading of milk exosomes with the rifamycin S derivatives compound 3 was successfully achieved,with a loading efficiency of 10.9％.The antimicrobial activity of the compound after exosome loading was significantly enhanced against Staphylococcus aureus in vitro and against Staphylococcus aureus residing within macrophages(P＜0.01).CONCLUSION The designed and synthesized derivatives of rifamycin S possess stronger anti-microbial activity,and their antibacterial efficacy against both extracellular and intracellular bacteria can be further enhanced after loading into exosomes.

BACKGROUND:Bone microenvironment is also rich in various immune cells and cytokines,which are closely related to bone cells and form an interactive network.Therefore,bone aging is not only caused by the senescence of osteocytes,but also accelerated by age-related changes in the immune system.OBJECTIVE:To review the age-related changes of bone marrow mesenchymal stem cells,osteoblasts,osteoclasts,and immune cells in the bone microenvironment,emphasize the key role of the immune microenvironment in bone aging,and the potential of immunotherapy in the treatment of bone aging.METHODS:We searched PubMed and China National Knowledge Infrastructure for articles on the interaction between bone cell senescence and immune cell senescence using"osteocytes,bone aging,immune microenvironment,immune cells,cytokines,immunosenescence,immunotherapy"as Chinese and English search terms.According to the inclusion and exclusion criteria,128 articles were finally included in the review.RESULTS AND CONCLUSION:Bone aging is a common pathological condition in the elderly,characterized by the interaction of multiple biological processes,among which immune factors play a key role.The cells,molecules,and signaling pathways in the immune microenvironment together constitute a complex network,and the imbalance of this network will accelerate the process of bone aging.The combination of anti-cellular aging and immunotherapy may bring new methods for the treatment of bone aging diseases,including the removal of senescent cells,targeted drugs for senescence-related secretory phenotypes,targeted therapy of inflammatory cytokines,immune cell regulation therapy,stem cell therapy,and molecular therapy.To more effectively and reasonably remove senescent cells,a deeper understanding of the mechanism of senescent cells is needed,which will help to identify senescent cells more accurately.Immunotherapy shows great potential and prospects in the treatment of bone aging,but there are some potential risks.It is believed that with the advancement of science and technology,people can more accurately understand the genetic information and immune status of the human body and develop more personalized immunotherapy plans.

Objective To preliminarily explore the safety of collecting colostrum in the third trimester,and to evaluate postpartum breastfeeding in pregnant women with hyperglycemia during pregnancy.Methods Pregnant women with hyperglycemia during pregnancy who had prenatal examinations in Obstetrics and Gynecology Hospital,Fudan University from Jul to Nov 2022 were prospectively divided into the colostrum collection group(n=52)in the third trimester and the control group(n=55)by randomized controlled grouping method.The t-test,χ2 test,Fisher's exact probability method and rank sum test were used for statistical analysis of the data to compare the delivery outcomes,neonatal outcomes and postpartum breastfeeding status between the two groups.Results There were no significant differences in the gestational weeks at delivery,delivery methods,breastfeeding rates at 42 days postpartum and 4 months postpartum between the two groups of pregnant women with hyperglycemia during pregnancy.There were also no significant differences in the Apgar scores at 1 minute and 5 minutes after birth and the neonatal hospitalization rate.The proportion of formula milk as the first supplementary feeding after delivery and the delayed lactation rate at 3 days postpartum in the colostrum collection group were significantly lower than those in the control group(P<0.05).The exclusive breastfeeding rates at 24 hours postpartum and 3 days postpartum in the colostrum collection group were significantly higher than those in the control group(P<0.05).Conclusion Collecting colostrum in the third trimester among pregnant women with hyperglycemia during pregnancy is safe,and it can reduce the rate of supplementary feeding with formula milk after delivery,and increase the exclusive breastfeeding rates at 24 hours postpartum and 3 days postpartum.

Objective:To analyze the factors associated with long-term survival after radical resection of hepatitis B virus(HBV)-associated hepatocellular carcinoma(HCC),and to construct random forest and Cox regression models,to evaluate the two models.Methods:A total of 368 patients with HBV-infected HCC who underwent radical resection were selected retrospectively.These patients were categorized as having a good prognosis(n=266)or a poor prognosis(n=102)based on their survival and mortality status.Univariate and Cox regression analysis were used to identify fac-tors that predict poor prognosis in HCC patients after surgery,and Cox regression and random forest prediction models were constructed and evaluated.Results:There were significant differences in smoking history,Child-Pugh classifica-tion,cirrhosis,microvascular invasion,TNM staging,tumor capsule integrity,platelet-to-lymphocyte ratio(PLR),regular antiviral therapy,HBV-DNA load,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR),systemic immune in-flammatory index(SII),and albumin-to-globulin ratio(AGR)between the two groups(P<0.05);Cox regression showed that cirrhosis,microvascular invasion,regular antiviral treatment,HBV-DNA load,NLR,PLR,SII,and AGR were related factors that negatively affected the prognosis of patients with HBV-infected HCC after surgery(P<0.05),with an AUC of 0.870 for predicting prognosis;the importance ranking obtained by the random forest model was HBV-DNA load,cirrho-sis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII,with an AUC of 0.926 for predicting prog-nosis;the AUC predicted by the random forest model was greater than that predicted by the Cox regression model(Z=2.411,P=0.016).Conclusion:HBV-DNA load,cirrhosis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII are factors that affect the poor prognosis of patients with HBV-related HCC after surgery.The random for-est prediction model constructed based on these factors has high predictive value and is superior to the Cox regression prediction model.